A computational theory of motor learning

In this paper we present a computational theory of human motor performance and learning. The theory is implemented as a running AI system called MAGGIE. Given a description of a desired movement as input, the system generates simulated motor behavior as output. The theory states that skills are encoded as motor schemas, which specify the positions and velocities of a limb at selected points in time. Moreover, there exist two natural representations for such knowledge: viewer-centered schemas describe visually perceived behavior, and joint-centered schemas are used to generate behavior. When the model acts upon these two representational formats, they exhibit quite different behavioral characteristics. MAGGIE performs the desired movement within a feedback control paradigm, monitoring for errors and correcting them when it detects them. Learning involves improving the joint-centered schema over many practice trials; this reduces the need for monitoring. The model accounts for a number of well-documented motor phenomena, including the speed-accuracy trade-off and the gradual improvement in performance with practice. It also makes several testable predictions. We close with a discussion of the theory's strengths and weaknesses, along with directions for future research

Precise control of flexible manipulators

Experimental apparatus were developed for physically testing control systems for pointing flexible structures, such as limber spacecraft, for the case that control actuators cannot be collocated with sensors. Structural damping ratios are less than 0.003, each basic configuration of sensor/actuator noncollocation is available, and inertias can be halved or doubled abruptly during control maneuvers, thereby imposing, in particular, a sudden reversal in the plant's pole-zero sequence. First experimental results are presented, including stable control with both collocation and noncollocation

Cooperative control theory and integrated flight and propulsion control

The major contribution of this research was the exposition of the fact that airframe and engine interactions could be present, and their effects could include loss of stability and performance of the control systems. Also, the significance of two directional, as opposed to one-directional, coupling was identified and explained. A multivariable stability and performance analysis methodology was developed, and applied to several candidate aircraft configurations. In these example evaluations, the significance of these interactions was underscored. Also exposed was the fact that with interactions present along with some integrated control approaches, the engine command/limiting logic (which represents an important nonlinear component of the engine control system) can impact closed-loop airframe/engine system stability. Finally, a brief investigation of control-law synthesis techniques appropriate for the class of systems was pursued, and it was determined that multivariable techniques, including model-following formulations of LQG and/or H infinity methods, showed promise. However, for practical reasons, decentralized control architectures are preferred, which is an architecture incompatible with these synthesis methods. The major contributions of the second phase of the grant was the development of conditions under which no decentralized controller could achieve closed loop system requirements on stability and/or performance. Sought were conditions that depended only on properties of the plant and the requirement, and independent of any particular control law or synthesis approach. Therefore, they could be applied a priori, before synthesis of a candidate control law. Under this grant, such conditions were found regarding stability, and encouraging initial results were obtained regarding performance

Dynamics of aerospace vehicles

The focus of this research was to address the modeling, including model reduction, of flexible aerospace vehicles, with special emphasis on models used in dynamic analysis and/or guidance and control system design. In the modeling, it is critical that the key aspects of the system being modeled be captured in the model. In this work, therefore, aspects of the vehicle dynamics critical to control design were important. In this regard, fundamental contributions were made in the areas of stability robustness analysis techniques, model reduction techniques, and literal approximations for key dynamic characteristics of flexible vehicles. All these areas are related. In the development of a model, approximations are always involved, so control systems designed using these models must be robust against uncertainties in these models

Preliminary design of a 100 kW turbine generator

The National Science Foundation and the Lewis Research Center have engaged jointly in a Wind Energy Program which includes the design and erection of a 100 kW wind turbine generator. The machine consists primarily of a rotor turbine, transmission, shaft, alternator, and tower. The rotor, measuring 125 feet in diameter and consisting of two variable pitch blades operates at 40 rpm and generates 100 kW of electrical power at 18 mph wind velocity. The entire assembly is placed on top of a tower 100 feet above ground level

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

Comments on the cybernetics of stability and regulation in social systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The methods and principles of cybernetics are applied to a discussion of stability and regulation in social systems taking a global viewpoint. The fundamental but still classical notion of stability as applied to homeostatic and ultrastable systems is discussed, with a particular reference to a specific well-studied example of a closed social group (the Tsembaga studied by Roy Rappaport in New Guinea). The discussion extends to the problem of evolution in large systems and the question of regulating evolution is addressed without special qualifications. A more comprehensive idea of stability is introduced as the argument turns to the problem of evolution for viability in general. Concepts pertaining to the problem of evolution are exemplified by a computer simulation model of an abstractly defined ecosystem in which various dynamic processes occur allowing the study of adaptive and evolutionary behaviour. In particular, the role of coalition formation and cooperative behaviour is stressed as a key factor in the evolution of complexity. The model consists of a population of several species of dimensionless automata inhabiting a geometrically defined environment in which a commodity essential for metabolic requirements (food) appears. Automata can sense properties of their environment, move about it, compete for food, reproduce or combine into coalitions thus forming new and more complex species. Each species is associated with a specific genotype from which the species’ behavioural characteristics (its phenotype) are derived. Complexity and survival efficiency of species increases through coalition formation, an event which occurs when automata are faced with an “undecidable” situation that is resolvable only by forming a new and more complex organization. Exogenous manipulation of the food distribution pattern and other critical factors produces different environmental conditions resulting in different behaviour patterns of automata and in different evolutionary “pathways.” Eve-1, the computer program developed to implement this model, accepts a high-level command language which allows for the setting of parameters, definition of initial configurations, and control of output formats. Results of simulation are produced graphically and include various pertinent tables. The program was given a modular hierarchical structure which allows easy generation of new versions incorporating different sets of rules. The model strives to capture the essence of the evolution of complexity viewed as a general process rather than to describe the evolution of a particular “real” system. In this respect it is not context-specific, and the behaviours which are observable in different runs can receive various interpretation depending on specific identifications. Of these, biological, ecological, and sociological interpretations are the most obvious and the latter, in particular, is stressed.J. M. Kaplan Fund in New Yor

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Spacecraft flight control system design selection process for a geostationary communication satellite

The Earth's first artificial satellite, Sputnik 1, slowly tumbled in orbit. The first U.S. satellite, Explorer 1, also tumbled out of control. Now, as we launch the Mars observer and the Cassini spacecraft, stability and control have become higher priorities. The flight control system design selection process is reviewed using as an example a geostationary communication satellite which is to have a life expectancy of 10 to 14 years. Disturbance torques including aerodynamic, magnetic, gravity gradient, solar, micrometeorite, debris, collision, and internal torques are assessed to quantify the disturbance environment so that the required compensating torque can be determined. Then control torque options, including passive versus active, momentum control, bias momentum, spin stabilization, dual spin, gravity gradient, magnetic, reaction wheels, control moment gyros, nutation dampers, inertia augmentation techniques, three-axis control, reactions control system (RCS), and RCS sizing, are considered. A flight control system design is then selected and preliminary stability criteria are met by the control gains selection